Systems and methods for a locking electric aircraft connector

ABSTRACT

An electric aircraft charging connector, including a set of pins, a sensor, a controller, and a locking mechanism. The set of pins may include an AC pin, the AC pin configured to supply AC power to a charging port and/or a DC pin, the DC pin configured to supply DC power to the charging port. The sensor can detect power flow from the pins to the charging port. The controller is communicatively connected to the sensor and configured to receive a signal from the sensor and send a locking signal to a locking mechanism. The locking mechanism is communicatively connected to the controller, having an engaged state wherein the charging connector is mechanically coupled to the charging port and a disengaged state wherein the charging connector is mechanically uncoupled form the charging port, the locking mechanism configured to receive a locking signal from the controller and enter the engaged state.

FIELD OF THE INVENTION

The present invention generally relates to the field of electricaircraft charging. In particular, the present invention is directed tosystems and methods for a locking electric aircraft charging connector.

BACKGROUND

In electrical aircraft charging systems, there exists a danger that thecharging connector may become detached or dislodged from the chargingport while the aircraft is charging. This may cause time to be lostwhere the aircraft is not charging when it is supposed to be charging.Additionally, there may be danger associated with a user unplugging thecharging connector from the charging port while the electric aircraft ischarging and there is current flowing from the charging connector to thecharging port. Existing solutions to this problem do not resolve theissue in a satisfactory manner.

SUMMARY OF THE DISCLOSURE

In an aspect, an electric vehicle charging connector including a set ofpins. The Set of pins including, an alternating current pin, thealternating current pin configured to supply alternating current powerto a charging port, and/or a direct current pin, the direct current pinconfigured to supply direct current power to the charging port. Theconnector also including a sensor, the sensor configured to detect theflow of power from the set of pins to the charging port, and acontroller, the controller communicatively connected to the sensor, thecontroller configured to receive a signal from the sensor and send alocking signal to a locking mechanism as a function of the signal fromthe sensor. The locking mechanism communicatively connected to thecontroller, the locking mechanism having an engaged state wherein thecharging connector is mechanically coupled to the charging port and adisengaged state wherein the charging connector is mechanicallyuncoupled form the charging port, the locking mechanism configured toreceive a locking signal from the controller and enter the engaged stateas a function of receiving the locking signal from the controller.

In another aspect, a method of electric vehicle charging including astep of monitoring a set of pins using a sensor to detect the flow ofpower from the set of pins to a charging port, the set of pins includingan alternating current pin, the alternating current pin configured tosupply alternating current power to the charging port, and/or a directcurrent pin, the direct current pin configured to supply direct currentpower to the charging port. The method further including a step ofsending a locking signal to a locking mechanism when the flow of powerfrom the set of pins to the charging port is detected. The method alsoincluding a step of engaging a locking mechanism, wherein the lockingmechanism has an engaged state wherein the charging connector ismechanically coupled to the charging port, the locking mechanism has adisengaged state wherein the charging connector is mechanicallyuncoupled from the charging port, and the locking mechanism iscommunicatively connected to a controller.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a diagram of an electric vehicle charging system having alocking bolt;

FIG. 2 is a diagram of a charging connector having a hook;

FIG. 3 is a diagram of a charging connector having electromagneticlocking systems;

FIG. 4 is a block diagram of an exemplary flight controller;

FIG. 5 is a block diagram of an exemplary machine learning module;

FIG. 6 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof; and

FIG. 7 is a flow diagram of a method of electric vehicle charging,including a locking mechanism.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed tosystems and methods for a locking electric vehicle charging connector.In an embodiment, the electric vehicle charging connector includes alocking mechanism that may mechanically couple to the charging port.

Aspects of the present disclosure can be used to automatically engage alocking mechanism to mechanically couple the charging connector to thecharging port. This is so, at least in part, because a sensor may detectwhen current is flowing between the charging connector and the chargingport. Thus, the locking mechanism may be automatically engaged when thesensor detects current or voltage, for example.

Referring now to FIG. 1 , a diagram of charging system 100 is shown.Charging system 100 includes charging connector 104. Charging connector104 may include an alternating current (AC) pin 108 and/or a directcurrent (DC) pin 112. AC pin 108 supplies AC power. For the purposes ofthis disclosure, “AC power” refers to electrical power provided with abi-directional flow of charge, where the flow of charge is periodicallyreversed. AC pin 108 may supply AC power at a variety of frequencies.For example, in a non-limiting embodiment, AC pin 108 may supply ACpower with a frequency of 50 Hz. In another non-limiting embodiment, ACpin 108 may supply AC power with a frequency of 60 Hz. One of ordinaryskill in the art, upon reviewing the entirety of this disclosure, wouldrealize that AC pin 108 may supply a wide variety of frequencies. ACpower produces a waveform when it is plotted out on a current vs. timeor voltage vs. time graph. In some embodiments, the waveform of the ACpower supplied by AC pin 108 may be a sine wave. In other embodiments,the waveform of the AC power supplied by AC pin 108 may be a squarewave. In some embodiments, the waveform of the AC power supplied by ACpin 108 may be a triangle wave. In yet other embodiments, the waveformof the AC power supplied by AC pin 108 may be a sawtooth wave. The ACpower supplied by AC pin 108 may, in general have any waveform, so longas the wave form produces a bi-directional flow of charge. AC power maybe provided without limitation, from alternating current generators,“mains” power provided over an AC power network from power plants, ACpower output by AC voltage converters including transformer-basedconverters, and/or AC power output by inverters that convert DC power,as described above, into AC power.

With continued reference to FIG. 1 , DC pin 112 supplies DC power. “DCpower,” for the purposes of this disclosure refers, to a one-directionalflow of charge. For example, in some embodiments, DC pin 112 may supplypower with a constant current and voltage. As another example, in otherembodiments, DC pin 112 may supply power with varying current andvoltage, or varying currant constant voltage, or constant currantvarying voltage. In another embodiment, when charging connector ischarging certain types of batteries, DC pin 112 may support a variedcharge pattern. This involves varying the voltage or currant suppliedduring the charging process in order to reduce or minimize batterydegradation. Examples of DC power flow include half-wave rectifiedvoltage, full-wave rectified voltage, voltage supplied from a battery orother DC switching power source, a DC converter such as a buck or boostconverter, voltage supplied from a DC dynamo or other generator, voltagefrom photovoltaic panels, voltage output by fuel cells, or the like. Forthe purposes of this disclosure, “supply,” “supplies,” “supplying,” andthe like, include both currently supplying and capable of supplying. Forexample, a live pin that “supplies” DC power need not be currentlysupplying DC power, it can also be capable of supplying DC power.

With continued reference to FIG. 1 , charging connector 104 may includea ground pin 116. Ground pin 116 is an electronic connector that isconnected to ground. For the purpose of this disclosure, “ground” is thereference point from which all voltages for a circuit are measured.“Ground” can include both a connection the earth, or a chassis ground,where all of the metallic parts in a device are electrically connectedtogether. In some embodiments, “ground” can be a floating ground. Groundmay alternatively or additionally refer to a “common” channel or“return” channel in some electronic systems. For instance, a chassisground may be a floating ground when the potential is not equal to earthground. In some embodiments, a negative pole in a DC circuit may begrounded. A “grounded connection,” for the purposes of this disclosure,is an electrical connection to “ground.” A circuit may be grounded inorder to increase safety in the event that a fault develops, to absorband reduce static charge, and the like. Speaking generally, a groundedconnection allows electricity to pass through the grounded connection toground instead of through, for example, a human that has come intocontact with the circuit. Additionally, grounding a circuit helps tostabilize voltages within the circuit.

With continued reference to FIG. 1 , charging connector 104 may includea variety of other pins. For example, charging connector 104 may includea proximity detection pin and/or a communication pin 120. Controller 124may receive a current datum from the proximity detection pin. In someembodiments, the proximity detection pin may be electrically connectedto the controller 124 to transmit current datum. Proximity detection pinhas no current flowing through it when charging connector 104 is notconnected to a port (e.g. charging port 304 in FIG. 3 ). Once chargingconnector 104 is connected to a plug, then proximity detection pin willhave current flowing through it, allowing for the controller to detect,using this current flow, that the charging connector 104 is connected toa plug. In some embodiments, current datum may be the measurement of thecurrent passing through proximity detection pin. In some embodimentsproximity detection pin may have a current sensor that generates thecurrent datum. In other embodiments, proximity detection sensor may beelectrically connected to controller and controller may include acurrent sensor. Communication pin 120 may transmit signals between anelectric vehicle and controller 124. Communication pin 120 may beelectrically or communicatively connected to controller 124.

With continued reference to FIG. 1 , charging system 100 includes asensor 128. In some embodiments, sensor 128 may be part of chargingconnector 104. As used in this disclosure, a “sensor” is a device thatis configured to detect an input and/or a phenomenon and transmitinformation related to the detection. Sensor 128 is communicativelyconnected to charging connector 104 and controller 124. Communicativelyconnected,” for the purpose of this disclosure, means connected suchthat data can be transmitted, whether wirelessly or wired. Sensor 128 isalso electrically connected to charging connector 104 such that it canmonitor the electrical connection between charger 144 and chargingconnector 104. In some embodiments, sensor may be configured to measurevoltage. As a nonlimiting example, sensor 128 may be a voltmeter. Insome embodiments, sensor 128 may be configured to measure current. As anonlimiting example, sensor may be an ammeter. Sensor 128 communicatesits readings to controller 124. In some embodiments, sensor 128 may beany sensor suitable for detecting electric movement, such as, as anon-limiting example. accelerometers, inertial measurement units (IMUs),pressure sensors, force sensors, proximity sensors, displacementsensors, or vibrational sensors, and the like.

With continued reference to FIG. 1 , sensor 128, in some embodiments,may be part of a sensor suite. Sensor suite may include a sensor orplurality thereof that may detect voltage, current, resistance,capacitance, temperature, or inductance; detection may be performedusing any suitable component, set of components, and/or mechanism fordirect or indirect measurement, including without limitationcomparators, analog to digital converters, any form of voltmeter, or thelike. Sensor suite may include digital sensors, analog sensors, or acombination thereof. Sensor suite may include digital-to-analogconverters (DAC), analog-to-digital converters (ADC, A/D, A-to-D), acombination thereof, or other signal conditioning components used intransmission of a resistance datum over wired or wireless connection.

With continued reference to FIG. 1 , Sensor suite may measure anelectrical property at an instant, over a period of time, orperiodically. Sensor suite may be configured to operate at any of thesedetection modes, switch between modes, or simultaneous measure in morethan one mode.

With continued reference to FIG. 1 , sensor suite may includethermocouples, thermistors, thermometers, passive infrared sensors,resistance temperature sensors (RTD's), semiconductor based integratedcircuits (IC), a combination thereof or another undisclosed sensor type,alone or in combination. Temperature, for the purposes of thisdisclosure, and as would be appreciated by someone of ordinary skill inthe art, is a measure of the heat energy of a system. Temperature, asmeasured by any number or combinations of sensors present within sensorsuite, may be measured in Fahrenheit (° F.), Celsius (° C.), Kelvin (°K), or another scale alone or in combination. The temperature measuredby sensors may comprise electrical signals which are transmitted totheir appropriate destination through a wireless or wired connection.

With continued reference to FIG. 1 , the charging connector 104 includesa locking mechanism 132. Locking mechanism 132 may take a variety offorms. Locking mechanism 132 may be consistent with any lockingmechanism disclosed as part of this disclosure. Locking mechanism 132has an engaged state and a disengaged state. In its engaged state,locking mechanism 132 mechanically couples charging connector 104 to acharging port on an electric vehicle. In the disengaged state of lockingmechanism 132, charging connector 104 is mechanically uncoupled from thecharging port. Locking mechanism 132 may receive a locking signal whichcauses locking mechanism 132 to enter its engaged state. In someembodiments, locking mechanism 132 may receive an unlocking signal whichmay cause locking mechanism 132 to enter its disengaged state. In someembodiments, the locking mechanism 132 includes a locking bolt 136.Locking bolt 136 may be actuated by an actuator 140. Actuator 140 mayreceive a locking signal and, accordingly, actuate locking bolt 136. Inthis case, this may be extending locking bolt 136 into a correspondingbolt hole or other corresponding hole or notch on a charging port. Whilelocking bolt 136 is extended, it may prevent charging connector 104 frombeing disconnected from a charging port. In some embodiments, lockingbolt 136 may also prevent rotation of charging connector 104 withrespect to a charging port when locking bolt 136 is extended. Actuator140 may receive an unlocking signal; as a response, actuator 140 mayretract locking bolt 136 from a charging port. This would allow chargingconnector 104 to be uncoupled from a charging port from the user.Actuator 140 may be any type of actuator capable of providing linearmotion to locking bolt 136. As non-limiting examples, actuator may be anelectric actuator, a hydraulic actuator, a mechanical actuator, orpneumatic actuator. After reviewing the entirety of this disclosure, aperson of ordinary skill in the art would appreciate that a variety ofactuators are suitable for this application. Locking mechanism 132 isdepicted as a locking bolt 136 and actuator 140 in FIG. 1 , but lockingmechanism 132 may include many other types of locking mechanisms, suchas a hook device (e.g. hook 228 in FIG. 2 ), or an electromagnetic lock(e.g. electromagnet 328 in FIG. 3 ).

With continued reference to FIG. 1 , although locking mechanism isdepicted as being part of charging connector 104, in some embodiments,locking mechanism may be part of a charging port. As a non-limitingexample, in some embodiments, a charging port may include a locking bolt136. In turn, charging connector 104 may include a corresponding bolthole, ridge, or other mating feature capable of receiving locking bolt136. In this manner, any of the locking mechanisms disclosed as part ofthis disclosure may be located on a charging port instead of a chargingconnector. Additionally, in some embodiments, locking mechanism 132 maybe located on the face of charging connector 104. As a non-limitingexample, in some embodiments, locking bolt 136 may be located on theface of charging connector 104. For the purposes of this disclosure, the“face” of charging connector 104 is the portion of charging connector104 the pins, such as AC pin 108 and DC pin 112, are disposed on. Inthese embodiments, a charging port may include a corresponding bracketon the face of the charging port with which to receive locking bolt 136.

With continued reference to FIG. 1 , charging system 100 may include acontroller 124. In some embodiments, controller 124 may be part ofcharging connector 104. Controller 124 is communicatively connected tosensor 128. Controller 124 is also communicatively connected to chargingconnector 104. Controller 124 receive signals from sensor 128. Thesesignals may contain any measurements that sensor 128 is making. As anon-limiting example, this may include current. As another non-limitingexample, this may include voltage. When controller 124 receives a signalfrom sensor 128 that indicates that electricity is flowing from charger144 to charging connector 104, controller 124 sends a locking signal toa locking mechanism 132 which is a component of charging connector 104.For example, a signal from sensor 128 indicating that there is voltagebelow a voltage threshold or current above a current threshold may causecontroller 124 to send the locking signal to locking mechanism 132. Insome embodiments, when controller 124 receives a signal from sensor 128that indicates that electricity is not flowing from charger 144 tocharging connector 104, controller 124 may send an unlocking signal tolocking mechanism 132. For example, a signal from sensor 128 indicatingthat there is voltage above a voltage threshold or current below acurrent threshold may cause controller 124 to send the unlocking signalto locking mechanism 132.

With continued reference to FIG. 1 , in some embodiments, controller 124may be implemented using an analog circuit. For example, in someembodiments controller 124 may be implemented using an analog circuitusing operational amplifiers, comparators, transistors, or the like. Insome embodiments, controller 124 may be implemented using a digitalcircuit having one or more logic gates. In some embodiments, controllermay be implemented using a combinational logic circuit, a synchronouslogic circuit, an asynchronous logic circuit, or the like. In otherembodiments, controller 124 may be implemented using an applicationspecific integrated circuit (ASIC). In yet other embodiments, controller124 may be implemented using a field programmable gate array (FPGA) andthe like.

With continued reference to FIG. 1 , in some embodiments, controller 124may be a computing device, flight controller, processor, controlcircuit, or the like. With continued reference to FIG. 1 , controller124 may include any computing device as described in this disclosure,including without limitation a microcontroller, microprocessor, digitalsignal processor (DSP) and/or system on a chip (SoC) as described inthis disclosure. Computing device may include, be included in, and/orcommunicate with a mobile device such as a mobile telephone orsmartphone. controller 124 may include a single computing deviceoperating independently, or may include two or more computing deviceoperating in concert, in parallel, sequentially or the like; two or morecomputing devices may be included together in a single computing deviceor in two or more computing devices. controller 124 may interface orcommunicate with one or more additional devices as described below infurther detail via a network interface device. Network interface devicemay be utilized for connecting controller 124 to one or more of avariety of networks, and one or more devices. Examples of a networkinterface device include, but are not limited to, a network interfacecard (e.g., a mobile network interface card, a LAN card), a modem, andany combination thereof. Examples of a network include, but are notlimited to, a wide area network (e.g., the Internet, an enterprisenetwork), a local area network (e.g., a network associated with anoffice, a building, a campus or other relatively small geographicspace), a telephone network, a data network associated with atelephone/voice provider (e.g., a mobile communications provider dataand/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network may employ a wiredand/or a wireless mode of communication. In general, any networktopology may be used. Information (e.g., data, software etc.) may becommunicated to and/or from a computer and/or a computing device.controller 124 may include but is not limited to, for example, acomputing device or cluster of computing devices in a first location anda second computing device or cluster of computing devices in a secondlocation. controller 124 may include one or more computing devicesdedicated to data storage, security, distribution of traffic for loadbalancing, and the like. controller 124 may distribute one or morecomputing tasks as described below across a plurality of computingdevices of computing device, which may operate in parallel, in series,redundantly, or in any other manner used for distribution of tasks ormemory between computing devices.

With continued reference to FIG. 1 , controller 124 may be configured toperform any method, method step, or sequence of method steps in anyembodiment described in this disclosure, in any order and with anydegree of repetition. For instance, controller 124 may be configured toperform a single step or sequence repeatedly until a desired orcommanded outcome is achieved; repetition of a step or a sequence ofsteps may be performed iteratively and/or recursively using outputs ofprevious repetitions as inputs to subsequent repetitions, aggregatinginputs and/or outputs of repetitions to produce an aggregate result,reduction or decrement of one or more variables such as globalvariables, and/or division of a larger processing task into a set ofiteratively addressed smaller processing tasks. controller 124 mayperform any step or sequence of steps as described in this disclosure inparallel, such as simultaneously and/or substantially simultaneouslyperforming a step two or more times using two or more parallel threads,processor cores, or the like; division of tasks between parallel threadsand/or processes may be performed according to any protocol suitable fordivision of tasks between iterations. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which steps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

With continued reference to FIG. 1 , in some embodiments, chargingsystem 100 may include a charger 144. Charger 144 can include an energystorage device or plurality of energy storage devices. As used in thisdisclosure, a “charger” is an electrical system and/or circuit thatincreases electrical energy in an energy store, for example a battery.In some embodiments, the energy storage device may be a battery. Charger144 may provide AC and/or DC power to charging connector 104. In someembodiments, charger 144 may include the ability to provide analternating current to direct current converter configured to convert anelectrical charging current from an alternating current. As used in thisdisclosure, an “analog current to direct current converter” is anelectrical component that is configured to convert analog current todigital current. An analog current to direct current (AC-DC) convertermay include an analog current to direct current power supply and/ortransformer. In some embodiments, charger 144 may have a connection togrid power component. Grid power component may be connected to anexternal electrical power grid. In some embodiments, grid powercomponent may be configured to slowly charge one or more batteries inorder to reduce strain on nearby electrical power grids. In oneembodiment, grid power component may have an AC grid current of at least450 amps. In some embodiments, grid power component may have an AC gridcurrent of more or less than 450 amps. In one embodiment, grid powercomponent may have an AC voltage connection of 480 Vac. In otherembodiments, grid power component may have an AC voltage connection ofabove or below 480 Vac. Charger 144 may be consistent with the chargerdisclosed in U.S. application Ser. No. 17/477,987 filed on Sep. 17,2021, titled “Systems and Methods for Adaptive Electric Vehiclereference. Additionally, charger 144 may be consistent with the chargerdisclosed in U.S. application Ser. No. 17/515,448 filed on Oct. 30,2021, titled “Systems and Methods for an Immediate Shutdown of anElectric Vehicle Charger,” the entirety of which is hereby incorporatedby reference.

Referring now to FIG. 2 , FIG. 2 depicts an exemplary embodiment of acharging connector 200 coupled to a charging port 204 by a lockingmechanism 208. Charging connector 200 may include a variety of pinsconfigured to mate with corresponding sockets on charging port 204. Inan exemplary embodiment, charging connector may include an AC pin 212, aDC pin 216, a ground pin 220 and a communication pin 224. AC pin 212 maybe consistent with any AC pin disclosed as part of this disclosure. DCpin 216 may be consistent with any DC pin disclosed as part of thisdisclosure. Ground pin 220 may be consistent with any ground pindisclosed as part of this disclosure. In some embodiments, chargingconnector 200 may include a proximity detection pin configured to detectwhen charging connector 200 is connected to charging port 204 (i.e. whenthe pins on charging connector 200 are mated with the correspondingsockets on charging port 204).

With continued reference to FIG. 2 , locking mechanism 208 may include ahook 228 and an actuator 232. Locking mechanism 208 may have an engagedstate and a disengaged state. In the engaged state of locking mechanism208, hook 228 may be retracted by actuator 232 such that the tip of hook228 is in a mating feature 236 on charging port 204. Mating feature 236may take a variety of forms. As non-limiting examples, mating feature236 may be a notch, a ridge, or the like. In this configuration,charging connector 200 is mechanically coupled to charging port 204 anda user cannot detach charging connector 200 from charging port 204.Actuator 232 retracts hook 228 in response to a locking signal from acontroller. When actuator 232 receives an unlocking signal from acontroller, actuator 232 may extend hook 228 so that it becomes releasedfrom mating feature 236. This is the disengaged state of lockingmechanism 208. In this state, a user may separate charging connector 200from charging port 204. In some embodiments, locking mechanism 208 mayinclude additional features to make it easier to fit the hook 228 oflocking mechanism 208 over charging port 204. As a non-limiting example,hook 228 could be attached to charging connector by a torsional springsuch that hook 228 may rotate outwards (counterclockwise) to fit aroundmating feature 236. The torsional spring may be biased in the clockwisedirects so that hook 228 rotates counterclockwise once it clears matingfeature 236 so that it is in proper alignment to mate with matingfeature 236. Actuator 232 may be consistent with any actuator disclosedas part of this disclosure. In some embodiments, locking mechanism 208may be consistent with any locking mechanism 208 disclosed as part ofthis disclosure.

Referring now to FIG. 3 , an exemplary embodiment of a chargingconnector 300 and a charging port 304 is depicted. Charging connector300 may include a variety of pins configured to mate with correspondingsockets on charging port 304. In an exemplary embodiment, chargingconnector may include an AC pin 308, a DC pin 312, a ground pin 316 anda communication pin 320. AC pin 308 may be consistent with any AC pindisclosed as part of this disclosure. DC pin 312 may be consistent withany DC pin disclosed as part of this disclosure. Ground pin 316 may beconsistent with any ground pin disclosed as part of this disclosure.Communication pin 320 may be consistent with any communication pindisclosed as part of this disclosure. In some embodiments, chargingconnector 300 may include a proximity detection pin configured to detectwhen charging connector 300 is connected to charging port 304 (i.e. whenthe pins on charging connector 300 are mated with the correspondingsockets on charging port 304).

With continued reference to FIG. 3 , charging connector 300 includes alocking mechanism 324. Locking mechanism 324 has an engaged state and adisengaged state. Locking mechanism 324 may include an electromagnet 328and a bolt 332. In its engaged state, power is supplied to electromagnet328. This causes electromagnet to create a magnetic field. This magneticfield attracts bolt 332 and causes it to be pulled into a hole 336 oncharging connector 300. In order to best realize this effect, bolt 332may be made of a ferrous metal, such as steel, carbon steel, alloysteel, or iron, and the like. In its disengaged state, power is notsupplied to electromagnet 328. This causes bolt 332 to not be attractedto electromagnet 328. Thus, bolt 332 may be removed from hole 336. Insome embodiments, bolt may be attached to a spring that automaticallyretracts bolt 332 when locking mechanism 324 is in its disengaged state.Electromagnet 328 may be electrically connected to a charger. Lockingmechanism 324 enters its engaged state when a controller sends a lockingsignal. In this case, the locking signal may be merely sendingelectricity to electromagnet 328. This may be done in a variety of ways.As a non-limiting example, a controller may send the locking signal to arelay. When the relay receives the locking signal, it may electricallyconnect electromagnet 328 to a power source, such as a charger. Lockingmechanism 324 may enter its disengaged state when a controller sends anunlocking signal. As a non-limiting example, a controller may send theunlocking signal to a relay. When the relay receives the unlockingsignal, relay may electrically disconnect electromagnet 328 from a powersource.

With continued reference to FIG. 3 , in some embodiments, lockingmechanism 324 may include a plate 340 instead of a bolt 332. Plate 340may be made of a ferrous metal, such as steel, carbon steel, alloysteel, or iron, and the like. In these embodiments, when electromagnet328 receives power, plate 340 is attracted to electromagnet 328. Thisholds plate 340 against electromagnet 328 such that a user cannotseparate charging connector 300 from charging port 304. Whenelectromagnet 328 does not receive power, then plate 340 is no longerattracted to electromagnet 328, meaning that a user may be able toseparate charging connector 300 from charging port 304. In addition,while particular embodiments of locking mechanism 324 are depicted inFIG. 3 , in other embodiments, locking mechanism 324 may be consistentwith any locking mechanism disclosed in this disclosure.

With continued reference to FIG. 3 , charging connector 300 may includean emergency release mechanism 344. Emergency release mechanism 344 maycause locking mechanism 324 to disengage. In an embodiment, emergencyrelease mechanism 344 may include a button. In this embodiment, thebutton may have an engaged state and a disengaged state. It itsdisengaged state, the button may connect electromagnet 328 to a powersource. In its disengaged state, the button may be unpressed. However,in its engaged state, the button may disconnect electromagnet 328 fromthe power source. The button enters its engaged state by being pressed.Thus, by pressing the button a user may disengage locking mechanism 324.In some embodiments, emergency release mechanism may be a lever.Flipping the lever may cause locking mechanism to be disconnected fromits power source. In some embodiments, emergency release mechanism 344may be located such that, when the emergency release mechanism istriggered (e.g. the button is pressed), power flow to the lockingmechanism 324 as well as AC pin 308 and/or DC pin 312 is disconnected.This ensures that power is not flowing from charging connector 300 tocharging port 304 when the user attempts to separate charging connector300 from charging port 304 having pressed the emergency releasemechanism. In some embodiments, emergency release mechanism 344 may bedesigned so that it cannot be accidentally engaged. As a non-limitingexample, emergency release mechanism 344 may be placed behind a panelthat must be opened to access emergency release mechanism 344. Asanother non-limiting example, emergency release mechanism 344 mayinclude several buttons that need to be pressed at the same time inorder to engage the emergency release mechanism 344. A person ofordinary skill in the art would appreciate, after having reviewed theentirety of this disclosure, would recognize that there are manypossible solutions for preventing the accidental engagement of theemergency release mechanism. In some embodiments, engaging emergencyrelease mechanism 344 may not include severing a power connection.Instead, in these cases, emergency release mechanism 344 may operate todisengage locking mechanism 324 manually. For instance, emergencyrelease mechanism 344 may be operated to disengage a locking mechanism324 such as a hook or bolt by disengaging said hook or bolt This may beaccomplished by, for example, flipping a lever or by other mechanicalmeans.

It should be noted that, in some embodiments, rather than the sensor andlocking mechanism communicating with a controller that is part of thecharging connector, the controller may be located within the charger. Itshould also be noted that, in some embodiments, rather than the sensorand locking mechanism communicating with a controller that is part ofthe charging connector, the controller may be located within theelectric vehicle. In this embodiment, as a non-limiting example, thecontroller may be a flight controller. In this embodiment, the sensorand locking mechanism could, as a non-limiting example, use acommunication pin disposed on the charging connector to communicate withthe controller.

Now referring to FIG. 4 , an exemplary embodiment 400 of a flightcontroller 404 is illustrated. As used in this disclosure a “flightcontroller” is a computing device of a plurality of computing devicesdedicated to data storage, security, distribution of traffic for loadbalancing, and flight instruction. Flight controller 404 may includeand/or communicate with any computing device as described in thisdisclosure, including without limitation a microcontroller,microprocessor, digital signal processor (DSP) and/or system on a chip(SoC) as described in this disclosure. Further, flight controller 404may include a single computing device operating independently, or mayinclude two or more computing device operating in concert, in parallel,sequentially or the like; two or more computing devices may be includedtogether in a single computing device or in two or more computingdevices. In embodiments, flight controller 404 may be installed in anaircraft, may control the aircraft remotely, and/or may include anelement installed in the aircraft and a remote element in communicationtherewith.

In an embodiment, and still referring to FIG. 4 , flight controller 404may include a signal transformation component 408. As used in thisdisclosure a “signal transformation component” is a component thattransforms and/or converts a first signal to a second signal, wherein asignal may include one or more digital and/or analog signals. Forexample, and without limitation, signal transformation component 408 maybe configured to perform one or more operations such as preprocessing,lexical analysis, parsing, semantic analysis, and the like thereof. Inan embodiment, and without limitation, signal transformation component408 may include one or more analog-to-digital convertors that transforma first signal of an analog signal to a second signal of a digitalsignal. For example, and without limitation, an analog-to-digitalconverter may convert an analog input signal to a 10-bit binary digitalrepresentation of that signal. In another embodiment, signaltransformation component 408 may include transforming one or morelow-level languages such as, but not limited to, machine languagesand/or assembly languages. For example, and without limitation, signaltransformation component 408 may include transforming a binary languagesignal to an assembly language signal. In an embodiment, and withoutlimitation, signal transformation component 408 may include transformingone or more high-level languages and/or formal languages such as but notlimited to alphabets, strings, and/or languages. For example, andwithout limitation, high-level languages may include one or more systemlanguages, scripting languages, domain-specific languages, visuallanguages, esoteric languages, and the like thereof. As a furthernon-limiting example, high-level languages may include one or morealgebraic formula languages, business data languages, string and listlanguages, object-oriented languages, and the like thereof.

Still referring to FIG. 4 , signal transformation component 408 may beconfigured to optimize an intermediate representation 412. As used inthis disclosure an “intermediate representation” is a data structureand/or code that represents the input signal. Signal transformationcomponent 408 may optimize intermediate representation as a function ofa data-flow analysis, dependence analysis, alias analysis, pointeranalysis, escape analysis, and the like thereof. In an embodiment, andwithout limitation, signal transformation component 408 may optimizeintermediate representation 412 as a function of one or more inlineexpansions, dead code eliminations, constant propagation, looptransformations, and/or automatic parallelization functions. In anotherembodiment, signal transformation component 408 may optimizeintermediate representation as a function of a machine dependentoptimization such as a peephole optimization, wherein a peepholeoptimization may rewrite short sequences of code into more efficientsequences of code. Signal transformation component 408 may optimizeintermediate representation to generate an output language, wherein an“output language,” as used herein, is the native machine language offlight controller 404. For example, and without limitation, nativemachine language may include one or more binary and/or numericallanguages.

In an embodiment, and without limitation, signal transformationcomponent 408 may include transform one or more inputs and outputs as afunction of an error correction code. An error correction code, alsoknown as error correcting code (ECC), is an encoding of a message or lotof data using redundant information, permitting recovery of corrupteddata. An ECC may include a block code, in which information is encodedon fixed-size packets and/or blocks of data elements such as symbols ofpredetermined size, bits, or the like. Reed-Solomon coding, in whichmessage symbols within a symbol set having q symbols are encoded ascoefficients of a polynomial of degree less than or equal to a naturalnumber k, over a finite field F with q elements; strings so encoded havea minimum hamming distance of k+1, and permit correction of (q−k−1)/2erroneous symbols. Block code may alternatively or additionally beimplemented using Golay coding, also known as binary Golay coding,Bose-Chaudhuri, Hocquenghuem (BCH) coding, multidimensional parity-checkcoding, and/or Hamming codes. An ECC may alternatively or additionallybe based on a convolutional code.

In an embodiment, and still referring to FIG. 4 , flight controller 404may include a reconfigurable hardware platform 416. A “reconfigurablehardware platform,” as used herein, is a component and/or unit ofhardware that may be reprogrammed, such that, for instance, a data pathbetween elements such as logic gates or other digital circuit elementsmay be modified to change an algorithm, state, logical sequence, or thelike of the component and/or unit. This may be accomplished with suchflexible high-speed computing fabrics as field-programmable gate arrays(FPGAs), which may include a grid of interconnected logic gates,connections between which may be severed and/or restored to program inmodified logic. Reconfigurable hardware platform 416 may be reconfiguredto enact any algorithm and/or algorithm selection process received fromanother computing device and/or created using machine-learningprocesses.

Still referring to FIG. 4 , reconfigurable hardware platform 416 mayinclude a logic component 420. As used in this disclosure a “logiccomponent” is a component that executes instructions on output language.For example, and without limitation, logic component may perform basicarithmetic, logic, controlling, input/output operations, and the likethereof. Logic component 420 may include any suitable processor, such aswithout limitation a component incorporating logical circuitry forperforming arithmetic and logical operations, such as an arithmetic andlogic unit (ALU), which may be regulated with a state machine anddirected by operational inputs from memory and/or sensors; logiccomponent 420 may be organized according to Von Neumann and/or Harvardarchitecture as a non-limiting example. Logic component 420 may include,incorporate, and/or be incorporated in, without limitation, amicrocontroller, microprocessor, digital signal processor (DSP), FieldProgrammable Gate Array (FPGA), Complex Programmable Logic Device(CPLD), Graphical Processing Unit (GPU), general purpose GPU, TensorProcessing Unit (TPU), analog or mixed signal processor, TrustedPlatform Module (TPM), a floating point unit (FPU), and/or system on achip (SoC). In an embodiment, logic component 420 may include one ormore integrated circuit microprocessors, which may contain one or morecentral processing units, central processors, and/or main processors, ona single metal-oxide-semiconductor chip. Logic component 420 may beconfigured to execute a sequence of stored instructions to be performedon the output language and/or intermediate representation 412. Logiccomponent 420 may be configured to fetch and/or retrieve the instructionfrom a memory cache, wherein a “memory cache,” as used in thisdisclosure, is a stored instruction set on flight controller 404. Logiccomponent 420 may be configured to decode the instruction retrieved fromthe memory cache to opcodes and/or operands. Logic component 420 may beconfigured to execute the instruction on intermediate representation 412and/or output language. For example, and without limitation, logiccomponent 420 may be configured to execute an addition operation onintermediate representation 412 and/or output language.

In an embodiment, and without limitation, logic component 420 may beconfigured to calculate a flight element 424. As used in this disclosurea “flight element” is an element of datum denoting a relative status ofaircraft. For example, and without limitation, flight element 424 maydenote one or more torques, thrusts, airspeed velocities, forces,altitudes, groundspeed velocities, directions during flight, directionsfacing, forces, orientations, and the like thereof. For example, andwithout limitation, flight element 424 may denote that aircraft iscruising at an altitude and/or with a sufficient magnitude of forwardthrust. As a further non-limiting example, flight status may denote thatis building thrust and/or groundspeed velocity in preparation for atakeoff. As a further non-limiting example, flight element 424 maydenote that aircraft is following a flight path accurately and/orsufficiently.

Still referring to FIG. 4 , flight controller 404 may include a chipsetcomponent 428. As used in this disclosure a “chipset component” is acomponent that manages data flow. In an embodiment, and withoutlimitation, chipset component 428 may include a northbridge data flowpath, wherein the northbridge dataflow path may manage data flow fromlogic component 420 to a high-speed device and/or component, such as aRAM, graphics controller, and the like thereof. In another embodiment,and without limitation, chipset component 428 may include a southbridgedata flow path, wherein the southbridge dataflow path may manage dataflow from logic component 420 to lower-speed peripheral buses, such as aperipheral component interconnect (PCI), industry standard architecture(ICA), and the like thereof. In an embodiment, and without limitation,southbridge data flow path may include managing data flow betweenperipheral connections such as ethernet, USB, audio devices, and thelike thereof. Additionally or alternatively, chipset component 428 maymanage data flow between logic component 420, memory cache, and a flightcomponent 432. As used in this disclosure a “flight component” is aportion of an aircraft that can be moved or adjusted to affect one ormore flight elements. For example, flight component 432 may include acomponent used to affect the aircrafts' roll and pitch which maycomprise one or more ailerons. As a further example, flight component432 may include a rudder to control yaw of an aircraft. In anembodiment, chipset component 428 may be configured to communicate witha plurality of flight components as a function of flight element 424.For example, and without limitation, chipset component 428 may transmitto an aircraft rotor to reduce torque of a first lift propulsor andincrease the forward thrust produced by a pusher component to perform aflight maneuver.

In an embodiment, and still referring to FIG. 4 , flight controller 404may be configured generate an autonomous function. As used in thisdisclosure an “autonomous function” is a mode and/or function of flightcontroller 404 that controls aircraft automatically. For example, andwithout limitation, autonomous function may perform one or more aircraftmaneuvers, take offs, landings, altitude adjustments, flight levelingadjustments, turns, climbs, and/or descents. As a further non-limitingexample, autonomous function may adjust one or more airspeed velocities,thrusts, torques, and/or groundspeed velocities. As a furthernon-limiting example, autonomous function may perform one or more flightpath corrections and/or flight path modifications as a function offlight element 424. In an embodiment, autonomous function may includeone or more modes of autonomy such as, but not limited to, autonomousmode, semi-autonomous mode, and/or non-autonomous mode. As used in thisdisclosure “autonomous mode” is a mode that automatically adjusts and/orcontrols aircraft and/or the maneuvers of aircraft in its entirety. Forexample, autonomous mode may denote that flight controller 404 willadjust the aircraft. As used in this disclosure a “semi-autonomous mode”is a mode that automatically adjusts and/or controls a portion and/orsection of aircraft. For example, and without limitation,semi-autonomous mode may denote that a pilot will control thepropulsors, wherein flight controller 404 will control the aileronsand/or rudders. As used in this disclosure “non-autonomous mode” is amode that denotes a pilot will control aircraft and/or maneuvers ofaircraft in its entirety.

In an embodiment, and still referring to FIG. 4 , flight controller 404may generate autonomous function as a function of an autonomousmachine-learning model. As used in this disclosure an “autonomousmachine-learning model” is a machine-learning model to produce anautonomous function output given flight element 424 and a pilot signal436 as inputs; this is in contrast to a non-machine learning softwareprogram where the commands to be executed are determined in advance by auser and written in a programming language. As used in this disclosure a“pilot signal” is an element of datum representing one or more functionsa pilot is controlling and/or adjusting. For example, pilot signal 436may denote that a pilot is controlling and/or maneuvering ailerons,wherein the pilot is not in control of the rudders and/or propulsors. Inan embodiment, pilot signal 436 may include an implicit signal and/or anexplicit signal. For example, and without limitation, pilot signal 436may include an explicit signal, wherein the pilot explicitly statesthere is a lack of control and/or desire for autonomous function. As afurther non-limiting example, pilot signal 436 may include an explicitsignal directing flight controller 404 to control and/or maintain aportion of aircraft, a portion of the flight plan, the entire aircraft,and/or the entire flight plan. As a further non-limiting example, pilotsignal 436 may include an implicit signal, wherein flight controller 404detects a lack of control such as by a malfunction, torque alteration,flight path deviation, and the like thereof. In an embodiment, andwithout limitation, pilot signal 436 may include one or more explicitsignals to reduce torque, and/or one or more implicit signals thattorque may be reduced due to reduction of airspeed velocity. In anembodiment, and without limitation, pilot signal 436 may include one ormore local and/or global signals. For example, and without limitation,pilot signal 436 may include a local signal that is transmitted by apilot and/or crew member. As a further non-limiting example, pilotsignal 436 may include a global signal that is transmitted by airtraffic control and/or one or more remote users that are incommunication with the pilot of aircraft. In an embodiment, pilot signal436 may be received as a function of a tri-state bus and/or multiplexorthat denotes an explicit pilot signal should be transmitted prior to anyimplicit or global pilot signal.

Still referring to FIG. 4 , autonomous machine-learning model mayinclude one or more autonomous machine-learning processes such assupervised, unsupervised, or reinforcement machine-learning processesthat flight controller 404 and/or a remote device may or may not use inthe generation of autonomous function. As used in this disclosure“remote device” is an external device to flight controller 404.Additionally or alternatively, autonomous machine-learning model mayinclude one or more autonomous machine-learning processes that afield-programmable gate array (FPGA) may or may not use in thegeneration of autonomous function. Autonomous machine-learning processmay include, without limitation machine learning processes such assimple linear regression, multiple linear regression, polynomialregression, support vector regression, ridge regression, lassoregression, elasticnet regression, decision tree regression, randomforest regression, logistic regression, logistic classification,K-nearest neighbors, support vector machines, kernel support vectormachines, naïve bayes, decision tree classification, random forestclassification, K-means clustering, hierarchical clustering,dimensionality reduction, principal component analysis, lineardiscriminant analysis, kernel principal component analysis, Q-learning,State Action Reward State Action (SARSA), Deep-Q network, Markovdecision processes, Deep Deterministic Policy Gradient (DDPG), or thelike thereof.

In an embodiment, and still referring to FIG. 4 , autonomous machinelearning model may be trained as a function of autonomous training data,wherein autonomous training data may correlate a flight element, pilotsignal, and/or simulation data to an autonomous function. For example,and without limitation, a flight element of an airspeed velocity, apilot signal of limited and/or no control of propulsors, and asimulation data of required airspeed velocity to reach the destinationmay result in an autonomous function that includes a semi-autonomousmode to increase thrust of the propulsors. Autonomous training data maybe received as a function of user-entered valuations of flight elements,pilot signals, simulation data, and/or autonomous functions. Flightcontroller 404 may receive autonomous training data by receivingcorrelations of flight element, pilot signal, and/or simulation data toan autonomous function that were previously received and/or determinedduring a previous iteration of generation of autonomous function.Autonomous training data may be received by one or more remote devicesand/or FPGAs that at least correlate a flight element, pilot signal,and/or simulation data to an autonomous function. Autonomous trainingdata may be received in the form of one or more user-enteredcorrelations of a flight element, pilot signal, and/or simulation datato an autonomous function.

Still referring to FIG. 4 , flight controller 404 may receive autonomousmachine-learning model from a remote device and/or FPGA that utilizesone or more autonomous machine learning processes, wherein a remotedevice and an FPGA is described above in detail. For example, andwithout limitation, a remote device may include a computing device,external device, processor, FPGA, microprocessor and the like thereof.Remote device and/or FPGA may perform the autonomous machine-learningprocess using autonomous training data to generate autonomous functionand transmit the output to flight controller 404. Remote device and/orFPGA may transmit a signal, bit, datum, or parameter to flightcontroller 404 that at least relates to autonomous function.Additionally or alternatively, the remote device and/or FPGA may providean updated machine-learning model. For example, and without limitation,an updated machine-learning model may be comprised of a firmware update,a software update, an autonomous machine-learning process correction,and the like thereof. As a non-limiting example a software update mayincorporate a new simulation data that relates to a modified flightelement. Additionally or alternatively, the updated machine learningmodel may be transmitted to the remote device and/or FPGA, wherein theremote device and/or FPGA may replace the autonomous machine-learningmodel with the updated machine-learning model and generate theautonomous function as a function of the flight element, pilot signal,and/or simulation data using the updated machine-learning model. Theupdated machine-learning model may be transmitted by the remote deviceand/or FPGA and received by flight controller 404 as a software update,firmware update, or corrected autonomous machine-learning model. Forexample, and without limitation autonomous machine learning model mayutilize a neural net machine-learning process, wherein the updatedmachine-learning model may incorporate a gradient boostingmachine-learning process.

Still referring to FIG. 4 , flight controller 404 may include, beincluded in, and/or communicate with a mobile device such as a mobiletelephone or smartphone. Further, flight controller may communicate withone or more additional devices as described below in further detail viaa network interface device. The network interface device may be utilizedfor commutatively connecting a flight controller to one or more of avariety of networks, and one or more devices. Examples of a networkinterface device include, but are not limited to, a network interfacecard (e.g., a mobile network interface card, a LAN card), a modem, andany combination thereof. Examples of a network include, but are notlimited to, a wide area network (e.g., the Internet, an enterprisenetwork), a local area network (e.g., a network associated with anoffice, a building, a campus or other relatively small geographicspace), a telephone network, a data network associated with atelephone/voice provider (e.g., a mobile communications provider dataand/or voice network), a direct connection between two computingdevices, and any combinations thereof. The network may include anynetwork topology and can may employ a wired and/or a wireless mode ofcommunication.

In an embodiment, and still referring to FIG. 4 , flight controller 404may include, but is not limited to, for example, a cluster of flightcontrollers in a first location and a second flight controller orcluster of flight controllers in a second location. Flight controller404 may include one or more flight controllers dedicated to datastorage, security, distribution of traffic for load balancing, and thelike. Flight controller 404 may be configured to distribute one or morecomputing tasks as described below across a plurality of flightcontrollers, which may operate in parallel, in series, redundantly, orin any other manner used for distribution of tasks or memory betweencomputing devices. For example, and without limitation, flightcontroller 404 may implement a control algorithm to distribute and/orcommand the plurality of flight controllers. As used in this disclosurea “control algorithm” is a finite sequence of well-defined computerimplementable instructions that may determine the flight component ofthe plurality of flight components to be adjusted. For example, andwithout limitation, control algorithm may include one or more algorithmsthat reduce and/or prevent aviation asymmetry. As a further non-limitingexample, control algorithms may include one or more models generated asa function of a software including, but not limited to Simulink byMathWorks, Natick, Mass., USA. In an embodiment, and without limitation,control algorithm may be configured to generate an auto-code, wherein an“auto-code,” is used herein, is a code and/or algorithm that isgenerated as a function of the one or more models and/or software's. Inanother embodiment, control algorithm may be configured to produce asegmented control algorithm. As used in this disclosure a “segmentedcontrol algorithm” is control algorithm that has been separated and/orparsed into discrete sections. For example, and without limitation,segmented control algorithm may parse control algorithm into two or moresegments, wherein each segment of control algorithm may be performed byone or more flight controllers operating on distinct flight components.

In an embodiment, and still referring to FIG. 4 , control algorithm maybe configured to determine a segmentation boundary as a function ofsegmented control algorithm. As used in this disclosure a “segmentationboundary” is a limit and/or delineation associated with the segments ofthe segmented control algorithm. For example, and without limitation,segmentation boundary may denote that a segment in the control algorithmhas a first starting section and/or a first ending section. As a furthernon-limiting example, segmentation boundary may include one or moreboundaries associated with an ability of flight component 432. In anembodiment, control algorithm may be configured to create an optimizedsignal communication as a function of segmentation boundary. Forexample, and without limitation, optimized signal communication mayinclude identifying the discrete timing required to transmit and/orreceive the one or more segmentation boundaries. In an embodiment, andwithout limitation, creating optimized signal communication furthercomprises separating a plurality of signal codes across the plurality offlight controllers. For example, and without limitation the plurality offlight controllers may include one or more formal networks, whereinformal networks transmit data along an authority chain and/or arelimited to task-related communications. As a further non-limitingexample, communication network may include informal networks, whereininformal networks transmit data in any direction. In an embodiment, andwithout limitation, the plurality of flight controllers may include achain path, wherein a “chain path,” as used herein, is a linearcommunication path comprising a hierarchy that data may flow through. Inan embodiment, and without limitation, the plurality of flightcontrollers may include an all-channel path, wherein an “all-channelpath,” as used herein, is a communication path that is not restricted toa particular direction. For example, and without limitation, data may betransmitted upward, downward, laterally, and the like thereof. In anembodiment, and without limitation, the plurality of flight controllersmay include one or more neural networks that assign a weighted value toa transmitted datum. For example, and without limitation, a weightedvalue may be assigned as a function of one or more signals denoting thata flight component is malfunctioning and/or in a failure state.

Still referring to FIG. 4 , the plurality of flight controllers mayinclude a master bus controller. As used in this disclosure a “masterbus controller” is one or more devices and/or components that areconnected to a bus to initiate a direct memory access transaction,wherein a bus is one or more terminals in a bus architecture. Master buscontroller may communicate using synchronous and/or asynchronous buscontrol protocols. In an embodiment, master bus controller may includeflight controller 404. In another embodiment, master bus controller mayinclude one or more universal asynchronous receiver-transmitters (UART).For example, and without limitation, master bus controller may includeone or more bus architectures that allow a bus to initiate a directmemory access transaction from one or more buses in the busarchitectures. As a further non-limiting example, master bus controllermay include one or more peripheral devices and/or components tocommunicate with another peripheral device and/or component and/or themaster bus controller. In an embodiment, master bus controller may beconfigured to perform bus arbitration. As used in this disclosure “busarbitration” is method and/or scheme to prevent multiple buses fromattempting to communicate with and/or connect to master bus controller.For example and without limitation, bus arbitration may include one ormore schemes such as a small computer interface system, wherein a smallcomputer interface system is a set of standards for physical connectingand transferring data between peripheral devices and master buscontroller by defining commands, protocols, electrical, optical, and/orlogical interfaces. In an embodiment, master bus controller may receiveintermediate representation 412 and/or output language from logiccomponent 420, wherein output language may include one or moreanalog-to-digital conversions, low bit rate transmissions, messageencryptions, digital signals, binary signals, logic signals, analogsignals, and the like thereof described above in detail.

Still referring to FIG. 4 , master bus controller may communicate with aslave bus. As used in this disclosure a “slave bus” is one or moreperipheral devices and/or components that initiate a bus transfer. Forexample, and without limitation, slave bus may receive one or morecontrols and/or asymmetric communications from master bus controller,wherein slave bus transfers data stored to master bus controller. In anembodiment, and without limitation, slave bus may include one or moreinternal buses, such as but not limited to a/an internal data bus,memory bus, system bus, front-side bus, and the like thereof. In anotherembodiment, and without limitation, slave bus may include one or moreexternal buses such as external flight controllers, external computers,remote devices, printers, aircraft computer systems, flight controlsystems, and the like thereof.

In an embodiment, and still referring to FIG. 4 , control algorithm mayoptimize signal communication as a function of determining one or morediscrete timings. For example, and without limitation master buscontroller may synchronize timing of the segmented control algorithm byinjecting high priority timing signals on a bus of the master buscontrol. As used in this disclosure a “high priority timing signal” isinformation denoting that the information is important. For example, andwithout limitation, high priority timing signal may denote that asection of control algorithm is of high priority and should be analyzedand/or transmitted prior to any other sections being analyzed and/ortransmitted. In an embodiment, high priority timing signal may includeone or more priority packets. As used in this disclosure a “prioritypacket” is a formatted unit of data that is communicated between theplurality of flight controllers. For example, and without limitation,priority packet may denote that a section of control algorithm should beused and/or is of greater priority than other sections.

Still referring to FIG. 4 , flight controller 404 may also beimplemented using a “shared nothing” architecture in which data iscached at the worker, in an embodiment, this may enable scalability ofaircraft and/or computing device. Flight controller 404 may include adistributer flight controller. As used in this disclosure a “distributerflight controller” is a component that adjusts and/or controls aplurality of flight components as a function of a plurality of flightcontrollers. For example, distributer flight controller may include aflight controller that communicates with a plurality of additionalflight controllers and/or clusters of flight controllers. In anembodiment, distributed flight control may include one or more neuralnetworks. For example, neural network also known as an artificial neuralnetwork, is a network of “nodes,” or data structures having one or moreinputs, one or more outputs, and a function determining outputs based oninputs. Such nodes may be organized in a network, such as withoutlimitation a convolutional neural network, including an input layer ofnodes, one or more intermediate layers, and an output layer of nodes.Connections between nodes may be created via the process of “training”the network, in which elements from a training dataset are applied tothe input nodes, a suitable training algorithm (such asLevenberg-Marquardt, conjugate gradient, simulated annealing, or otheralgorithms) is then used to adjust the connections and weights betweennodes in adjacent layers of the neural network to produce the desiredvalues at the output nodes. This process is sometimes referred to asdeep learning.

Still referring to FIG. 4 , a node may include, without limitation aplurality of inputs x_(i) that may receive numerical values from inputsto a neural network containing the node and/or from other nodes. Nodemay perform a weighted sum of inputs using weights w_(i) that aremultiplied by respective inputs x_(i). Additionally or alternatively, abias b may be added to the weighted sum of the inputs such that anoffset is added to each unit in the neural network layer that isindependent of the input to the layer. The weighted sum may then beinput into a function φ, which may generate one or more outputs y.Weight w_(i) applied to an input x_(i) may indicate whether the input is“excitatory,” indicating that it has strong influence on the one or moreoutputs y, for instance by the corresponding weight having a largenumerical value, and/or a “inhibitory,” indicating it has a weak effectinfluence on the one more inputs y, for instance by the correspondingweight having a small numerical value. The values of weights w_(i) maybe determined by training a neural network using training data, whichmay be performed using any suitable process as described above. In anembodiment, and without limitation, a neural network may receivesemantic units as inputs and output vectors representing such semanticunits according to weights w_(i) that are derived using machine-learningprocesses as described in this disclosure.

Still referring to FIG. 4 , flight controller may include asub-controller 440. As used in this disclosure a “sub-controller” is acontroller and/or component that is part of a distributed controller asdescribed above; for instance, flight controller 404 may be and/orinclude a distributed flight controller made up of one or moresub-controllers. For example, and without limitation, sub-controller 440may include any controllers and/or components thereof that are similarto distributed flight controller and/or flight controller as describedabove. Sub-controller 440 may include any component of any flightcontroller as described above. Sub-controller 440 may be implemented inany manner suitable for implementation of a flight controller asdescribed above. As a further non-limiting example, sub-controller 440may include one or more processors, logic components and/or computingdevices capable of receiving, processing, and/or transmitting dataacross the distributed flight controller as described above. As afurther non-limiting example, sub-controller 440 may include acontroller that receives a signal from a first flight controller and/orfirst distributed flight controller component and transmits the signalto a plurality of additional sub-controllers and/or flight components.

Still referring to FIG. 4 , flight controller may include aco-controller 444. As used in this disclosure a “co-controller” is acontroller and/or component that joins flight controller 404 ascomponents and/or nodes of a distributer flight controller as describedabove. For example, and without limitation, co-controller 444 mayinclude one or more controllers and/or components that are similar toflight controller 404. As a further non-limiting example, co-controller444 may include any controller and/or component that joins flightcontroller 404 to distributer flight controller. As a furthernon-limiting example, co-controller 444 may include one or moreprocessors, logic components and/or computing devices capable ofreceiving, processing, and/or transmitting data to and/or from flightcontroller 404 to distributed flight control system. Co-controller 444may include any component of any flight controller as described above.Co-controller 444 may be implemented in any manner suitable forimplementation of a flight controller as described above.

In an embodiment, and with continued reference to FIG. 4 , flightcontroller 404 may be designed and/or configured to perform any method,method step, or sequence of method steps in any embodiment described inthis disclosure, in any order and with any degree of repetition. Forinstance, flight controller 404 may be configured to perform a singlestep or sequence repeatedly until a desired or commanded outcome isachieved; repetition of a step or a sequence of steps may be performediteratively and/or recursively using outputs of previous repetitions asinputs to subsequent repetitions, aggregating inputs and/or outputs ofrepetitions to produce an aggregate result, reduction or decrement ofone or more variables such as global variables, and/or division of alarger processing task into a set of iteratively addressed smallerprocessing tasks. Flight controller may perform any step or sequence ofsteps as described in this disclosure in parallel, such assimultaneously and/or substantially simultaneously performing a step twoor more times using two or more parallel threads, processor cores, orthe like; division of tasks between parallel threads and/or processesmay be performed according to any protocol suitable for division oftasks between iterations. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various ways in whichsteps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

Referring now to FIG. 5 , an exemplary embodiment of a machine-learningmodule 500 that may perform one or more machine-learning processes asdescribed in this disclosure is illustrated. Machine-learning module mayperform determinations, classification, and/or analysis steps, methods,processes, or the like as described in this disclosure using machinelearning processes. A “machine learning process,” as used in thisdisclosure, is a process that automatedly uses training data 504 togenerate an algorithm that will be performed by a computingdevice/module to produce outputs 508 given data provided as inputs 512;this is in contrast to a non-machine learning software program where thecommands to be executed are determined in advance by a user and writtenin a programming language.

Still referring to FIG. 5 , “training data,” as used herein, is datacontaining correlations that a machine-learning process may use to modelrelationships between two or more categories of data elements. Forinstance, and without limitation, training data 504 may include aplurality of data entries, each entry representing a set of dataelements that were recorded, received, and/or generated together; dataelements may be correlated by shared existence in a given data entry, byproximity in a given data entry, or the like. Multiple data entries intraining data 504 may evince one or more trends in correlations betweencategories of data elements; for instance, and without limitation, ahigher value of a first data element belonging to a first category ofdata element may tend to correlate to a higher value of a second dataelement belonging to a second category of data element, indicating apossible proportional or other mathematical relationship linking valuesbelonging to the two categories. Multiple categories of data elementsmay be related in training data 504 according to various correlations;correlations may indicate causative and/or predictive links betweencategories of data elements, which may be modeled as relationships suchas mathematical relationships by machine-learning processes as describedin further detail below. Training data 504 may be formatted and/ororganized by categories of data elements, for instance by associatingdata elements with one or more descriptors corresponding to categoriesof data elements. As a non-limiting example, training data 504 mayinclude data entered in standardized forms by persons or processes, suchthat entry of a given data element in a given field in a form may bemapped to one or more descriptors of categories. Elements in trainingdata 504 may be linked to descriptors of categories by tags, tokens, orother data elements; for instance, and without limitation, training data504 may be provided in fixed-length formats, formats linking positionsof data to categories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),JavaScript Object Notation (JSON), or the like, enabling processes ordevices to detect categories of data.

Alternatively or additionally, and continuing to refer to FIG. 5 ,training data 504 may include one or more elements that are notcategorized; that is, training data 504 may not be formatted or containdescriptors for some elements of data. Machine-learning algorithmsand/or other processes may sort training data 504 according to one ormore categorizations using, for instance, natural language processingalgorithms, tokenization, detection of correlated values in raw data andthe like; categories may be generated using correlation and/or otherprocessing algorithms. As a non-limiting example, in a corpus of text,phrases making up a number “n” of compound words, such as nouns modifiedby other nouns, may be identified according to a statisticallysignificant prevalence of n-grams containing such words in a particularorder; such an n-gram may be categorized as an element of language suchas a “word” to be tracked similarly to single words, generating a newcategory as a result of statistical analysis. Similarly, in a data entryincluding some textual data, a person's name may be identified byreference to a list, dictionary, or other compendium of terms,permitting ad-hoc categorization by machine-learning algorithms, and/orautomated association of data in the data entry with descriptors or intoa given format. The ability to categorize data entries automatedly mayenable the same training data 504 to be made applicable for two or moredistinct machine-learning algorithms as described in further detailbelow. Training data 504 used by machine-learning module 500 maycorrelate any input data as described in this disclosure to any outputdata as described in this disclosure. As a non-limiting illustrativeexample flight elements and/or pilot signals may be inputs, wherein anoutput may be an autonomous function.

Further referring to FIG. 5 , training data may be filtered, sorted,and/or selected using one or more supervised and/or unsupervisedmachine-learning processes and/or models as described in further detailbelow; such models may include without limitation a training dataclassifier 516. Training data classifier 516 may include a “classifier,”which as used in this disclosure is a machine-learning model as definedbelow, such as a mathematical model, neural net, or program generated bya machine learning algorithm known as a “classification algorithm,” asdescribed in further detail below, that sorts inputs into categories orbins of data, outputting the categories or bins of data and/or labelsassociated therewith. A classifier may be configured to output at leasta datum that labels or otherwise identifies a set of data that areclustered together, found to be close under a distance metric asdescribed below, or the like. Machine-learning module 500 may generate aclassifier using a classification algorithm, defined as a processeswhereby a computing device and/or any module and/or component operatingthereon derives a classifier from training data 504. Classification maybe performed using, without limitation, linear classifiers such aswithout limitation logistic regression and/or naive Bayes classifiers,nearest neighbor classifiers such as k-nearest neighbors classifiers,support vector machines, least squares support vector machines, fisher'slinear discriminant, quadratic classifiers, decision trees, boostedtrees, random forest classifiers, learning vector quantization, and/orneural network-based classifiers. As a non-limiting example, trainingdata classifier 416 may classify elements of training data tosub-categories of flight elements such as torques, forces, thrusts,directions, and the like thereof.

Still referring to FIG. 5 , machine-learning module 500 may beconfigured to perform a lazy-learning process 520 and/or protocol, whichmay alternatively be referred to as a “lazy loading” or“call-when-needed” process and/or protocol, may be a process wherebymachine learning is conducted upon receipt of an input to be convertedto an output, by combining the input and training set to derive thealgorithm to be used to produce the output on demand. For instance, aninitial set of simulations may be performed to cover an initialheuristic and/or “first guess” at an output and/or relationship. As anon-limiting example, an initial heuristic may include a ranking ofassociations between inputs and elements of training data 504. Heuristicmay include selecting some number of highest-ranking associations and/ortraining data 504 elements. Lazy learning may implement any suitablelazy learning algorithm, including without limitation a K-nearestneighbors algorithm, a lazy naïve Bayes algorithm, or the like; personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various lazy-learning algorithms that may be applied togenerate outputs as described in this disclosure, including withoutlimitation lazy learning applications of machine-learning algorithms asdescribed in further detail below.

Alternatively or additionally, and with continued reference to FIG. 5 ,machine-learning processes as described in this disclosure may be usedto generate machine-learning models 524. A “machine-learning model,” asused in this disclosure, is a mathematical and/or algorithmicrepresentation of a relationship between inputs and outputs, asgenerated using any machine-learning process including withoutlimitation any process as described above, and stored in memory; aninput is submitted to a machine-learning model 524 once created, whichgenerates an output based on the relationship that was derived. Forinstance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination ofinput data using coefficients derived during machine-learning processesto calculate an output datum. As a further non-limiting example, amachine-learning model 524 may be generated by creating an artificialneural network, such as a convolutional neural network comprising aninput layer of nodes, one or more intermediate layers, and an outputlayer of nodes. Connections between nodes may be created via the processof “training” the network, in which elements from a training data 504set are applied to the input nodes, a suitable training algorithm (suchas Levenberg-Marquardt, conjugate gradient, simulated annealing, orother algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning.

Still referring to FIG. 5 , machine-learning algorithms may include atleast a supervised machine-learning process 528. At least a supervisedmachine-learning process 528, as defined herein, include algorithms thatreceive a training set relating a number of inputs to a number ofoutputs, and seek to find one or more mathematical relations relatinginputs to outputs, where each of the one or more mathematical relationsis optimal according to some criterion specified to the algorithm usingsome scoring function. For instance, a supervised learning algorithm mayinclude flight elements and/or pilot signals as described above asinputs, autonomous functions as outputs, and a scoring functionrepresenting a desired form of relationship to be detected betweeninputs and outputs; scoring function may, for instance, seek to maximizethe probability that a given input and/or combination of elements inputsis associated with a given output to minimize the probability that agiven input is not associated with a given output. Scoring function maybe expressed as a risk function representing an “expected loss” of analgorithm relating inputs to outputs, where loss is computed as an errorfunction representing a degree to which a prediction generated by therelation is incorrect when compared to a given input-output pairprovided in training data 504. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variouspossible variations of at least a supervised machine-learning process528 that may be used to determine relation between inputs and outputs.Supervised machine-learning processes may include classificationalgorithms as defined above.

Further referring to FIG. 5 , machine learning processes may include atleast an unsupervised machine-learning processes 532. An unsupervisedmachine-learning process, as used herein, is a process that derivesinferences in datasets without regard to labels; as a result, anunsupervised machine-learning process may be free to discover anystructure, relationship, and/or correlation provided in the data.Unsupervised processes may not require a response variable; unsupervisedprocesses may be used to find interesting patterns and/or inferencesbetween variables, to determine a degree of correlation between two ormore variables, or the like.

Still referring to FIG. 5 , machine-learning module 500 may be designedand configured to create a machine-learning model 524 using techniquesfor development of linear regression models. Linear regression modelsmay include ordinary least squares regression, which aims to minimizethe square of the difference between predicted outcomes and actualoutcomes according to an appropriate norm for measuring such adifference (e.g. a vector-space distance norm); coefficients of theresulting linear equation may be modified to improve minimization.Linear regression models may include ridge regression methods, where thefunction to be minimized includes the least-squares function plus termmultiplying the square of each coefficient by a scalar amount topenalize large coefficients. Linear regression models may include leastabsolute shrinkage and selection operator (LASSO) models, in which ridgeregression is combined with multiplying the least-squares term by afactor of 1 divided by double the number of samples. Linear regressionmodels may include a multi-task lasso model wherein the norm applied inthe least-squares term of the lasso model is the Frobenius normamounting to the square root of the sum of squares of all terms. Linearregression models may include the elastic net model, a multi-taskelastic net model, a least angle regression model, a LARS lasso model,an orthogonal matching pursuit model, a Bayesian regression model, alogistic regression model, a stochastic gradient descent model, aperceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g. a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure.

Continuing to refer to FIG. 5 , machine-learning algorithms may include,without limitation, linear discriminant analysis. Machine-learningalgorithm may include quadratic discriminate analysis. Machine-learningalgorithms may include kernel ridge regression. Machine-learningalgorithms may include support vector machines, including withoutlimitation support vector classification-based regression processes.Machine-learning algorithms may include stochastic gradient descentalgorithms, including classification and regression algorithms based onstochastic gradient descent. Machine-learning algorithms may includenearest neighbors algorithms. Machine-learning algorithms may includeGaussian processes such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naïve Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision treeclassification or regression algorithms. Machine-learning algorithms mayinclude ensemble methods such as bagging meta-estimator, forest ofrandomized tress, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 6 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 600 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 600 includes a processor 604 and a memory608 that communicate with each other, and with other components, via abus 612. Bus 612 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Processor 604 may include any suitable processor, such as withoutlimitation a processor incorporating logical circuitry for performingarithmetic and logical operations, such as an arithmetic and logic unit(ALU), which may be regulated with a state machine and directed byoperational inputs from memory and/or sensors; processor 604 may beorganized according to Von Neumann and/or Harvard architecture as anon-limiting example. Processor 604 may include, incorporate, and/or beincorporated in, without limitation, a microcontroller, microprocessor,digital signal processor (DSP), Field Programmable Gate Array (FPGA),Complex Programmable Logic Device (CPLD), Graphical Processing Unit(GPU), general purpose GPU, Tensor Processing Unit (TPU), analog ormixed signal processor, Trusted Platform Module (TPM), a floating pointunit (FPU), and/or system on a chip (SoC).

Memory 608 may include various components (e.g., machine-readable media)including, but not limited to, a random-access memory component, a readonly component, and any combinations thereof. In one example, a basicinput/output system 616 (BIOS), including basic routines that help totransfer information between elements within computer system 600, suchas during start-up, may be stored in memory 608. Memory 608 may alsoinclude (e.g., stored on one or more machine-readable media)instructions (e.g., software) 620 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 608 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 600 may also include a storage device 624. Examples of astorage device (e.g., storage device 624) include, but are not limitedto, a hard disk drive, a magnetic disk drive, an optical disc drive incombination with an optical medium, a solid-state memory device, and anycombinations thereof. Storage device 624 may be connected to bus 612 byan appropriate interface (not shown). Example interfaces include, butare not limited to, SCSI, advanced technology attachment (ATA), serialATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and anycombinations thereof. In one example, storage device 624 (or one or morecomponents thereof) may be removably interfaced with computer system 600(e.g., via an external port connector (not shown)). Particularly,storage device 624 and an associated machine-readable medium 628 mayprovide nonvolatile and/or volatile storage of machine-readableinstructions, data structures, program modules, and/or other data forcomputer system 600. In one example, software 620 may reside, completelyor partially, within machine-readable medium 628. In another example,software 620 may reside, completely or partially, within processor 604.

Computer system 600 may also include an input device 632. In oneexample, a user of computer system 600 may enter commands and/or otherinformation into computer system 600 via input device 632. Examples ofan input device 632 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 632may be interfaced to bus 612 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 612, and any combinations thereof. Input device 632 mayinclude a touch screen interface that may be a part of or separate fromdisplay 636, discussed further below. Input device 632 may be utilizedas a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 600 via storage device 624 (e.g., a removable disk drive, a flashdrive, etc.) and/or network interface device 640. A network interfacedevice, such as network interface device 640, may be utilized forconnecting computer system 600 to one or more of a variety of networks,such as network 644, and one or more remote devices 648 connectedthereto. Examples of a network interface device include, but are notlimited to, a network interface card (e.g., a mobile network interfacecard, a LAN card), a modem, and any combination thereof. Examples of anetwork include, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network, such as network 644,may employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, software 620,etc.) may be communicated to and/or from computer system 600 via networkinterface device 640.

Computer system 600 may further include a video display adapter 652 forcommunicating a displayable image to a display device, such as displaydevice 636. Examples of a display device include, but are not limitedto, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasmadisplay, a light emitting diode (LED) display, and any combinationsthereof. Display adapter 652 and display device 636 may be utilized incombination with processor 604 to provide graphical representations ofaspects of the present disclosure. In addition to a display device,computer system 600 may include one or more other peripheral outputdevices including, but not limited to, an audio speaker, a printer, andany combinations thereof. Such peripheral output devices may beconnected to bus 612 via a peripheral interface 656. Examples of aperipheral interface include, but are not limited to, a serial port, aUSB connection, a FIREWIRE connection, a parallel connection, and anycombinations thereof.

Referring now to FIG. 7 , a flowchart of a method of electric vehiclecharging 700 is shown. Method 700 includes a step 705 of monitoring aset of pins using a sensor to detect the flow of power from the set ofpins to a charging port. The sensor may be consistent with any sensordisclosed as part of this disclosure. The charging port may beconsistent with any charging port disclosed in this disclosure. Chargingport is disposed on an electric vehicle. In some embodiments, theelectric vehicle may be an electric airplane. The set of pins mayinclude an alternating current (AC) pin, the AC pin configured to supplyAC power to the charging port. The set of pins also may include a directcurrent (DC) pin, the DC pin configured to supply DC power to thecharging port. AC pin may be consistent with any AC pin disclosed inthis disclosure. DC pin may be consistent with any DC pin disclosed inthis disclosure. Method also includes a step 710 of sending a lockingsignal to a locking mechanism when the flow of power from the set ofpins to the charging port is detected. The locking signal may beconsistent with any locking signal disclosed in this disclosure. Thelocking mechanism may be consistent with any locking mechanism disclosedin this disclosure. Method 700 furthermore includes a step 715 ofengaging a locking mechanism. The locking mechanism has an engaged statewherein the charging connector is mechanically coupled to the chargingport. Charging connector may be consistent with any charging connectordisclosed as part of this disclosure. The engaged state of the lockingmechanism may be consistent with the engaged state of any lockingmechanism disclosed as part of this disclosure. The locking mechanismalso has a disengaged state wherein the charging connector ismechanically uncoupled from the charging port. The disengaged state ofthe locking mechanism may be consistent with the disengaged state of anylocking mechanism disclosed as part of this disclosure. The lockingmechanism is communicatively connected to a controller. The controllermay be consistent with any controller disclosed as part of thisdisclosure.

With continued reference to FIG. 7 , in some embodiments, method 700 mayfurther include a step of sending an unlocking signal to the lockingmechanism when the flow of power from the set of pins to the chargingport is not detected. Unlocking signal may be consistent with anyunlocking signal disclosed as part of this disclosure. Method 700, insome embodiments, may include a further step of disengaging the lockingmechanism. In some embodiments, locking mechanism may include a magnet,such as, for example, electromagnet 328 in FIG. 3 . In some embodiments,locking mechanism may include a locking bolt such as, for example,locking bolt 136 in FIG. 1 . In some embodiments, locking mechanism mayinclude a hook, such as, for instance, hook 228 in FIG. 2 . These aremerely examples of possible elements of the locking mechanism; manyother possibilities are possible.

With continued reference to FIG. 7 , in some embodiments of method 700,the sensor may be configured to measure current. In some embodiments ofmethod 700, the sensor may be configured to measure voltage.

With continued reference to FIG. 7 , in some embodiments, method 700 mayfurther include a step of disengaging an emergency release mechanism.This step of disengaging an emergency release mechanism may also includedisengaging the locking mechanism such that the charging connector ismechanically uncoupled from the charging port. In some embodiments, theemergency release mechanism may be a button. The button may have anengaged state and a disengaged state. In the engaged state, the chargingconnector may be mechanically coupled to the charging port. In thedisengaged state, the charging connector may be mechanically uncoupledfrom the charging port. In some embodiments, the step of disengaging theemergency release mechanism further includes severing a chargingconnection between the charging connector and the charging port.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods andsystems according to the present disclosure. Accordingly, thisdescription is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

What is claimed is:
 1. An electric aircraft charging connector,comprising: a set of pins, the set of pins comprising: an alternatingcurrent pin, the alternating current pin configured to supplyalternating current power to a charging port of an electric aircraft;and a direct current pin, the direct current pin configured to supplydirect current power to the charging port of the electric aircraft; asensor, the sensor configured to detect a flow of power from the set ofpins to the charging port of the electric aircraft; a locking mechanism,the locking mechanism having an engaged state wherein the chargingconnector is mechanically coupled to the charging port and a disengagedstate wherein the charging connector is mechanically uncoupled form thecharging port, the locking mechanism configured to: receive a lockingsignal; and enter the engaged state as a function of receiving thelocking signal; and a controller, the controller communicativelyconnected to the sensor and the locking mechanism, the controllerconfigured to: receive a signal from the sensor; and send the lockingsignal to the locking mechanism as a function of the signal from thesensor, wherein the charging connector is distinct from the electricaircraft.
 2. The electric aircraft charging connector of claim 1,wherein: the controller is further configured to send an unlockingsignal to the locking mechanism as a function of the signal from thesensor; and the locking mechanism is further configured to: receive theunlocking signal from the controller; and enter the disengaged state asa function of receiving the unlocking signal from the controller.
 3. Theelectric aircraft charging connector of claim 1, wherein the sensor isconfigured to measure voltage.
 4. The electric aircraft chargingconnector of claim 1, wherein the sensor is configured to measureelectric current.
 5. The electric aircraft charging connector of claim1, further comprising an emergency release mechanism, the emergencyrelease mechanism configured to switch the locking mechanism from itsengaged state to its disengaged state.
 6. The electric aircraft chargingconnector of claim 5, wherein the emergency release mechanism includes abutton having an engaged state and a disengaged state, wherein thebutton in its engaged state causes the locking mechanism to enter itsdisengaged state.
 7. The electric aircraft charging connector of claim5, wherein the emergency release mechanism is further configured tosever a charging connection between the charging connector and thecharging port.
 8. The electric aircraft charging connector of claim 1,wherein the locking mechanism is distinct from the electric aircraft andcomprises a magnet.
 9. The electric aircraft charging connector of claim1, wherein the locking mechanism is distinct from the electric aircraftand comprises a locking bolt.
 10. The electric aircraft chargingconnector of claim 1, wherein the locking mechanism comprises a hook.11. A method of electric aircraft charging, comprising: monitoring a setof pins, of a charging connector, using a sensor to detect a flow ofpower from the set of pins to a charging port of an electric aircraft,the set of pins comprising: an alternating current pin, the alternatingcurrent pin configured to supply alternating current power to thecharging port of the electric aircraft; and a direct current pin, thedirect current pin configured to supply direct current power to thecharging port of the electric aircraft; sending a locking signal to alocking mechanism, of the charging connector, when the flow of powerfrom the set of pins to the charging port is detected; and engaging thelocking mechanism, wherein: the locking mechanism has an engaged statewherein the charging connector is mechanically coupled to the chargingport; the locking mechanism has a disengaged state wherein the chargingconnector is mechanically uncoupled from the charging port; and thelocking mechanism is communicatively connected to a controller, whereinthe charging connector is distinct from the electric aircraft.
 12. Themethod of electric aircraft charging of claim 11, further comprising:sending an unlocking signal to the locking mechanism when the flow ofpower from the set of pins to the charging port is not detected; anddisengaging the locking mechanism.
 13. The method of electric aircraftcharging of claim 11, wherein the sensor is configured to measurevoltage.
 14. The method of electric aircraft charging of claim 11,wherein the sensor is configured to measure electric current.
 15. Themethod of electric aircraft charging of claim 11, further comprisingdisengaging an emergency release mechanism, wherein disengaging theemergency release mechanism comprises switching the locking mechanismfrom its engaged state to its disengaged state.
 16. The method ofelectric aircraft charging of claim 15, wherein the emergency releasemechanism is a button having an engaged state and a disengaged state,wherein the button in its engaged state causes the locking mechanism toenter its disengaged state.
 17. The method of electric aircraft chargingof claim 15, wherein disengaging the emergency release mechanism furthercomprises severing a charging connection between the charging connectorand the charging port.
 18. The method of electric aircraft charging ofclaim 11, wherein the locking mechanism is distinct from the electricaircraft and comprises a magnet.
 19. The method of electric aircraftcharging of claim 11, wherein the locking mechanism is distinct from theelectric aircraft and comprises a locking bolt.
 20. The method ofelectric aircraft charging of claim 11, wherein the locking mechanismcomprises a hook.